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<p>A secure, scalable, and energy-efficient model is essential for data aggregation in Industrial Wireless Sensor Networks (IWSNs), where challenges such as confidentiality, energy optimization, node failures, network congestion, and dynamic conditions must be addressed. This work proposes a novel architecture for secure data aggregation and transmission, integrating Internet of Things (IoT) capabilities with Genetic Algorithm (GA)-based optimization to improve energy efficiency and system reliability. The proposed approach is Secure and Adaptive Genetic-optimized Energy-aware IWSN (SAGE-IWSN) consists of four key modules. In the first module, 1000 wireless nodes are deployed in the NS-2 simulator, along with 30 heterogeneous analog sensors within the IoT ecosystem for real-time data collection. Preprocessing operations such as noise removal and normalization are performed to ensure high-quality input. The second module applies GA for optimal cluster head (CH) selection using a multi-objective fitness function that minimizes energy consumption, balances cluster load, and reduces intra-cluster distances. CHs aggregate intra-cluster data, reducing redundancy and improving communication efficiency. The third module establishes a broadcast tree to connect CHs with the base station through shortest path routing, optimized via QoS parameters including reliability, energy, and delay. The final module evaluates performance using energy expenditure, network lifetime, throughput, latency, and packet delivery ratio (PDR). Results demonstrate a 35% reduction in energy consumption, a 25% increase in network lifetime, a 15% improvement in PDR, and a 20% reduction in latency compared to LEACH, PEGASIS and HEED. Additionally, metrics such as Energy-Delay Product (EDP) and Normalized Routing Load (NRL) show significant improvements. Integrated SHA-256 authentication ensures lightweight security while preserving throughput. Overall, the proposed system achieves secure, efficient, and intelligent data transmission, making it highly applicable to smart manufacturing, environmental monitoring, and infrastructure automation.</p>

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Secure and Scalable Data Aggregation Protocol with Energy Optimization in Industrial Wireless Networks

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Vidya H. Deshmukh,
  • Promad J. Deore,
  • S. B. Deosarkar

摘要

A secure, scalable, and energy-efficient model is essential for data aggregation in Industrial Wireless Sensor Networks (IWSNs), where challenges such as confidentiality, energy optimization, node failures, network congestion, and dynamic conditions must be addressed. This work proposes a novel architecture for secure data aggregation and transmission, integrating Internet of Things (IoT) capabilities with Genetic Algorithm (GA)-based optimization to improve energy efficiency and system reliability. The proposed approach is Secure and Adaptive Genetic-optimized Energy-aware IWSN (SAGE-IWSN) consists of four key modules. In the first module, 1000 wireless nodes are deployed in the NS-2 simulator, along with 30 heterogeneous analog sensors within the IoT ecosystem for real-time data collection. Preprocessing operations such as noise removal and normalization are performed to ensure high-quality input. The second module applies GA for optimal cluster head (CH) selection using a multi-objective fitness function that minimizes energy consumption, balances cluster load, and reduces intra-cluster distances. CHs aggregate intra-cluster data, reducing redundancy and improving communication efficiency. The third module establishes a broadcast tree to connect CHs with the base station through shortest path routing, optimized via QoS parameters including reliability, energy, and delay. The final module evaluates performance using energy expenditure, network lifetime, throughput, latency, and packet delivery ratio (PDR). Results demonstrate a 35% reduction in energy consumption, a 25% increase in network lifetime, a 15% improvement in PDR, and a 20% reduction in latency compared to LEACH, PEGASIS and HEED. Additionally, metrics such as Energy-Delay Product (EDP) and Normalized Routing Load (NRL) show significant improvements. Integrated SHA-256 authentication ensures lightweight security while preserving throughput. Overall, the proposed system achieves secure, efficient, and intelligent data transmission, making it highly applicable to smart manufacturing, environmental monitoring, and infrastructure automation.